Latte: Use-Case Assistive-Service Driven Automated Accessibility Testing Framework for Android

For 15% of the world population with disabilities, accessibility is arguably the most critical software quality attribute. The ever-growing reliance of users with disability on mobile apps further underscores the need for accessible software in this domain. Existing automated accessibility assessment techniques primarily aim to detect violations of predefined guidelines, thereby produce a massive amount of accessibility warnings that often overlook the way software is actually used by users with disability. This paper presents a novel, high-fidelity form of accessibility testing for Android apps, called Latte, that automatically reuses tests written to evaluate an app’s functional correctness to assess its accessibility as well. Latte first extracts the use case corresponding to each test, and then executes each use case in the way disabled users would, i.e., using assistive services. Our empirical evaluation on real-world Android apps demonstrates Latte’s effectiveness in detecting substantially more useful defects than prior techniques.

Video Presentation:

Artifacts

The artifacts are available for download from here.

Publications

More details about this project can be found in our publication below:

• Latte: Use-Case Assistive-Service Driven Automated Accessibility Testing Framework for Android
  Navid Salehnamadi, Abdulaziz Alshayban, Jun-Wei Lin, Iftekhar Ahmed, Stacy Branham, and Sam Malek
  ACM Conference on Human Factors in Computing System (CHI 2021), Yokohama, Japan, May 2021. (26% acceptance rate)
  [PDF]